Members who want to pay can pay
Discovery delivered. Funded work under way. All results below are committed targets.
After a new CRM went live, members of a professional membership body could not reliably renew, join or reach their accounts, putting subscription income at risk at the moment members tried to pay. We separated the failures the data causes from those it does not, and committed to fixing the data failures at source, with named owners so they stay fixed.
For a membership body, renewal is the income. After the new CRM went live on migrated data, renewal, joining and account access failed often enough to put subscription income at risk. Members who cannot log in start to question why they belong.
Duplicate records locked members out of their accounts and sent them double invoices. Partner records did not match. Issues sat across several trackers and systems, and nobody owned any of them, so the same problems kept coming back.
Two causes, with different owners. Data was migrated without the reconciliation, deduplication and ownership that would have caught these defects, and nobody owned the issues once they appeared. Most of the other failures were system, integration or change request problems, owned by the organisation's digital team.
- Profile the data, and bring every function that touches a member into the same room.
- Sort every failing step by cause: data, system, integration or process. Commit only to what the data causes, and hand the rest to its owner.
- Draw the scope around the cause, and write down what is out.
- Define every target so it can be tested, not argued about.
- Name Data Owners for membership, partner and contact data, and replace scattered trackers with one owned register.
- Put two signatures on every correction: the process owner and the data owner.
- Measure whether new defects keep arriving, so the clean-up is not undone.
Most of the broken steps were not data
It would have been easy to take every failure as a data brief, and then be judged on problems we did not own. We sorted each by cause and handed the rest to their owners. Sort every failure by cause before you promise to fix it.
A target you can test
A percentage target means nothing until you say what it counts. We defined each target against the failing records behind a priority item, so success can be proven, not claimed.
Postage is the wrong benefit
The usual case for cleaning addresses and duplicates is postage saved. For a membership body, the benefit that matters is renewal income protected. State the benefit in terms the board already watches.
Stop the data drifting back
Light governance is the part leadership cuts first, because it looks like meetings. We gave it a hard measure: new defects entering the register each sprint. If that does not fall, the clean-up is being undone as fast as it is done.
Two signatures on one decision
We correct the data, the client applies it, and process owners change the rules that broke it. Every correction needs a process signature and a data signature, so the fix and the cause are dealt with together.
Everything below is committed for 31/12/2026, and we will publish results after that date.
- Duplicate records that block renewal cut back (Committed)
- Partner records reconciled (Committed)
- More members reachable at a valid address (Committed)
- One issue register, with every issue owned (Committed)
- Fewer failed journeys where the data is the cause (Committed)
- New defects falling sprint on sprint (Committed)
Double Lock on every correction.
Sort every failure by cause before you promise to fix it: commit only to what the data causes.
Committed: renewal income protected at the moment members pay, and a digital team no longer chasing problems that were never theirs. Every data failure is fixed at source, with an owner to keep it fixed. We will publish results after 31/12/2026.
Tell us which number you do not trust. We will tell you what owning it is worth.
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